Principles of artificial intelligence
Principles of artificial intelligence
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Model-based estimation techniques for 3-D reconstruction from projections
Machine Vision and Applications
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Computer Vision
ACM Transactions on Graphics (TOG)
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Multidimensional Systems and Signal Processing
A robust technique for surface reconstruction from orthogonal slices
Machine Graphics & Vision International Journal
Extracting surface representations from rim curves
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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An estimation approach is described for three-dimensional reconstruction from line integral projections using incomplete and very noisy data. Generalized cylinders parameterized by stochastic dynamic models are used to represent prior knowledge about the properties of objects of interest in the probed domain. The object models, a statistical measurement model, and the maximum a posteriori probability performance criterion are combined to reformulate the reconstruction problem as a computationally challenging nonlinear estimation problem. For computational feasibility, a suboptimal hierarchical algorithm is described whose individual steps are locally optimal and are combined to satisfy a global optimality criterion. The formulation and algorithm are restricted to objects whose center axis is a single-valued function of a fixed spatial coordinate. Simulation examples demonstrate accurate reconstructions with as few as four views in a 135 degrees sector, at an average signal-to-noise ratio of 3.3.